Aristolochic acids (AAs) are established human carcinogens strongly associated with upper tract urothelial carcinoma (UTUC). However, the multi-target oncogenic network beyond their genotoxic mechanism remains incompletely elucidated. This study employed an integrated computational approach combining network toxicology, machine learning, molecular docking, and molecular dynamics (MD) simulations to systematically explore the potential molecular mechanisms of AA-induced UTUC. We identified 97 shared potential targets of AAs and UTUC. Enrichment analyses revealed their significant involvement in lipid metabolism, xenobiotic detoxification, and cancer-related pathways such as PI3K-Akt signaling. Topological analysis of the protein-protein interaction network and a nested cross-validation machine learning model highlighted five core genes: CASP3, EGFR, PARP1, PTGS2, and HSP90AA1. Molecular docking predicted high binding affinities of AA with these core targets, particularly for PTGS2 (-9.3 kcal/mol) and EGFR (-8.2 kcal/mol). Subsequent 100-ns MD simulations and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations confirmed the structural stability and spontaneous binding (ΔG_bind = -55.68 kcal/mol) of the AA-EGFR complex. Our multi-omics analysis suggests that AAs may promote UTUC not only via canonical DNA adduct formation but also potentially through direct interactions with key signaling proteins, implicating a synergistic mechanism involving both genotoxic and non-genotoxic pathways. These findings provide a theoretical foundation for novel preventive and therapeutic strategies against AA-associated UTUC.

INTRODUCTION:Hypothesis-driven studies have identified many modifiable cancer risk factors, but research focusing on single exposures overlooks their complex interactions. OBJECTIVES:This study applied an exposome-wide approach across multiple cancer types to systematically identify modifiable exposures and evaluate their combined effects with genetic susceptibility. METHODS:We analyzed data from over 460,000 UK Biobank participants with 15 years of follow-up, assessing 93 modifiable exposures in relation to 23 site-specific cancers using Cox models. Exposomic risk scores (ERS) were constructed to quantify the combined effects of identified factors. Population attributable fractions (PAFs) were calculated to estimate the potential population-level burden associated with these factors. Finally, polygenic risk scores (PRS) were incorporated to evaluate the relative contributions of genetic susceptibility and modifiable exposures to cancer risk. RESULTS:We identified 209 significant exposure-cancer risk estimates, with both their number and magnitude varying markedly across cancer types. Lung cancer exhibited the largest number of associations, whereas ovarian, testicular, and brain cancers showed no significant associations. Several exposures were associated with multiple cancers, such as basal metabolic rate, smoking, diabetes, household income, alcohol consumption, and body fat percentage, suggesting pleiotropic effects. ERSs summarized the combined influence of these modifiable exposures, and PAFs estimated their potential contribution to the population-level cancer burden across cancer types (4.5%-75.6%). PRS integration showed larger relative genetic contributions for prostate cancer (48.6%), melanoma (48.9%), and Hodgkin lymphoma (61%), while modifiable exposures were more influential in endometrial cancer (74.2%), lung cancer (71.1%), and liver cancer (57.8%). CONCLUSION:This study provides a pan-cancer, exposome-wide perspective on modifiable and genetic contributions to cancer risk, highlighting the potential value of targeting controllable exposures to reduce population-level cancer burden.

BACKGROUND:The pathophysiological changes driving incident kidney cancer remain unclear. This study aimed to identify protein biomarkers and underlying mechanisms using pre-diagnostic plasma proteomics. MATERIALS AND METHODS:Among 48 851 UK Biobank participants, 165 were diagnosed with kidney cancer, and 2911 plasma proteins were analyzed. Dynamic changes in significant proteins were assessed up to 15 years before diagnosis using locally estimated scatterplot smoothing method. A mediation analysis using a four-component framework was conducted to evaluate the mediating role of proteomic features in the associations of body mass index (BMI) and smoking with kidney cancer risk. Additionally, an absolute shrinkage and selection operator regression model was developed for proteomics-based risk prediction. RESULTS:Over a follow-up period exceeding 11 years, 24 proteins were significantly associated with kidney cancer risk ( P < 0.05, Bonferroni-corrected for 2911), with Hepatitis A Virus Cellular Receptor 1 (HAVCR1) exhibiting the most statistically significant association (HR = 3.18, 95% CI: 2.70-3.74, P = 1.11 × 10 -40 ). Trajectory modeling revealed that HAVCR1 exhibited the most significant fluctuations, with abnormal expression detectable up to 15 years before diagnosis. Unsupervised clustering identified four distinct protein trajectory patterns, suggesting different mechanisms may drive kidney cancer progression at various stages. Proteomic data mediated the effects of BMI and smoking on cancer risk, contributing 38.6% and 9.2% to the risk, respectively. The proteomic model significantly improved kidney cancer risk prediction compared to the clinical model (concordance index [C-index]: 0.811 vs. 0.713, P = 0.029), with HAVCR1 alone demonstrating comparable discriminative ability (C-index: 0.754). CONCLUSIONS:This large-scale plasma proteomics study highlights the potential of biomarkers, particularly HAVCR1, for early detection and insight into kidney cancer pathophysiology.

INTRODUCTION:This study aimed to evaluate the prognostic impact of computed tomography (CT)-defined sarcopenia, assessed via different muscle groups (total abdominal muscle [TAM], psoas muscle [PM], and paraspinal muscle [PS]), on outcomes in patients with sarcomatoid renal cell carcinoma (SRCC) undergoing surgical treatment. PATIENTS AND METHODS:A retrospective cohort of 125 pathologically confirmed SRCC patients (2009-2024) was analyzed. Sarcopenia was defined using sex-specific cut-offs for height-adjusted TAM and PM indices, and absolute PS area at the third lumbar vertebra on preoperative CT. The primary endpoint was overall survival (OS). Statistical analyses included Kaplan-Meier curves, Cox regression, LASSO selection, and bootstrap-validated nomogram construction. RESULTS:Multivariable analysis identified PS-defined sarcopenia as an independent predictor of worse OS (HR = 2.74, 95% CI, 1.001-7.481, P < .05), while TAM- and PM-defined sarcopenia lacked independent prognostic value. Subgroup analysis revealed significant prognostic association of PS-sarcopenia only in male patients. A nomogram incorporating tumor size, N stage, M stage, platelet-neutrophil ratio, platelet-albumin ratio, and PS-sarcopenia demonstrated good predictive accuracy for 1-, 2-, and 3-year OS (AUCs: 0.807, 0.760, 0.781), with a bootstrap-corrected C-index of 0.750. CONCLUSION:CT-quantified paraspinal muscle mass is an independent prognostic factor for OS in SRCC patients after surgery, particularly in males, and may serve as a valuable biomarker for risk stratification. A nomogram incorporating PS-sarcopenia shows promising predictive performance for individualized prognosis.

PURPOSE:To evaluate the prognostic impact of preoperative muscle depletion (including sarcopenia and myosteatosis) in patients with bladder cancer (BCa) after radical cystectomy (RC). METHODS:We retrospectively reviewed 185 patients undergoing RC for urothelial carcinoma. We used the computed tomography images at the L3 level of patients to get the skeletal muscle index (SMI) and skeletal muscle density (SMD). Sarcopenia is defined by the SMI while myosteatosis is defined by SMD. We used univariate Cox regression analysis to identify risk factors and included these risk factors in a multivariate Cox regression analysis to calculate the hazard ratio (HR) and 95% confidence interval (95% CI). RESULTS:In the univariate Cox analysis, sarcopenia (P < .001) and myosteatosis (P = .017) were both associated with poorer overall survival (OS). Meanwhile, sarcopenia (P < .001) and myosteatosis (P = .019) were both associated with poorer progression-free survival (PFS). In the multivariate Cox analysis, sarcopenia was identified as an independent risk factor for both OS (P = .018) and PFS (P = .005), whereas myosteatosis was not an independent risk factor for OS (P = .225) or PFS (P = .104). CONCLUSIONS:Preoperative muscle depletion (including sarcopenia and myosteatosis) significantly correlates with poor prognosis of patients undergoing RC. Sarcopenia is an in dependent risk factor for 5-year OS and PFS. Our nomogram models demonstrated good predictive accuracy. Preoperative identification of muscle depletion and tailored interventions (exercise and nutrition) may improve postoperative outcomes.

BackgroundPapillary renal cell carcinoma (pRCC) exhibits significant heterogeneity, and robust prognostic tools specifically validated for this subtype are lacking. The GRANT score, incorporating grade, age, nodes, and tumor stage, shows promise but requires extensive validation in pRCC-specific cohorts. This study aimed to evaluate the prognostic value of the GRANT score and develop a novel nomogram for predicting survival in pRCC patients.MethodsA multi-center retrospective study was conducted. Patients undergoing surgery for pRCC were identified from the SEER database (2004-2015) and formed the training (n=4,001) and internal validation (n=1,689) cohorts. An external validation cohort (n=151) was sourced from a Chinese institution. Overall survival (OS) and cancer-specific survival (CSS) were primary endpoints. The GRANT score was calculated for all patients. Univariate and multivariate Cox analyses identified independent prognostic factors, which were incorporated into nomograms for predicting 1-, 3-, and 5-year OS and CSS. Model performance was assessed using the concordance index (C-index), time-dependent receiver operating characteristic curves, and calibration plots.ResultsMultivariate analysis confirmed the GRANT score as an independent prognostic factor for both OS and CSS. The prognostic nomograms integrated key variables, including surgical approach, marital status, TNM stage, tumor size, Fuhrman grade, and the GRANT score. For OS prediction, the nomogram achieved C-indices of 0.711 (training), 0.720 (internal validation), and 0.740 (external validation). For CSS prediction, the model demonstrated superior performance, with C-indices of 0.860 (training), 0.873 (internal validation), and 0.826 (external validation). Calibration curves showed excellent agreement between predicted and observed outcomes. Risk stratification based on nomogram scores effectively distinguished low-, intermediate-, and high-risk patient groups with significantly different survival.ConclusionThis study validates the GRANT score as an independent prognostic factor in a large pRCC cohort. The developed and externally validated nomogram provides a clinically useful tool with robust performance, particularly for predicting CSS, facilitating personalized risk assessment and postoperative management for pRCC patients.

Background:Elderly patients (≥70 years) with bladder cancer undergoing radical cystectomy (RC) represent a vulnerable population with heterogeneous outcomes. Traditional pathological lymph node (pN) staging has limitations. This study evaluated the prognostic value of lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) compared to pN and developed a novel nomogram for this demographic. Methods:Using data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database [2004-2015], 1,018 elderly bladder cancer patients post-RC were identified and randomly split into training (n=712) and internal validation (n=306) cohorts. An independent external cohort (n=260) was included. Predictive performance of pN, LNR, and LODDS was assessed using time-dependent area under the curve (AUC) and concordance index (C-index). Prognostic factors were identified via least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression. A nomogram predicting 1-, 3-, and 5-year overall survival (OS) was constructed and validated. Results:LODDS demonstrated superior prognostic discrimination compared to pN and LNR across all cohorts (training C-index: LODDS 0.602 vs. pN 0.573 vs. LNR 0.579). The final nomogram incorporated race, tumor stage (T stage), metastasis stage (M stage), chemotherapy status, and LODDS. It showed robust performance: training C-index =0.647 [95% confidence interval (CI): 0.622-0.672], internal validation C-index 0.650 (95% CI: 0.611-0.690), and external validation C-index =0.729 (95% CI: 0.687-0.770). Calibration curves indicated strong agreement between predicted and observed survival. LODDS maintained superior stratification within the ≥80-year subgroup. Risk stratification based on the nomogram significantly differentiated survival outcomes (log-rank P<0.001). Conclusions:LODDS provides enhanced prognostic stratification over pN and LNR in elderly bladder cancer patients post-RC. The developed and validated LODDS-based nomogram offers a practical tool for individualized survival prediction, aiding clinical decision-making in this growing population.

Inflammation impacts the prognosis of numerous types of tumors. Inflammatory indicators such as the neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and neutrophil-to-eosinophil ratio (NER) have emerged as potential prognostic markers and are closely correlated with the outcomes of cancer patients. However, the connection between NER and cancer prognosis remains incompletely understood. Therefore, we conducted a meta-analysis to investigate the potential of the inflammatory marker NER as a prognostic indicator in cancer patients. A thorough search was conducted across PubMed, Embase, Web of Science, and the Cochrane Library, with a cutoff date of August 2024. Relevant data were extracted, and hazard ratios (HRs) and relative risks (RRs), along with their corresponding 95

OBJECTIVES:It has been proposed that metastasectomy may cure some patients with a single metastasis. Hence, we considered it necessary to clarify the role of metastasectomy for metastatic bladder cancer (BCa). METHODS:We conducted a systematic review of published reports on the efficacy of metastasectomy for different sites of BCa metastasis. We searched English articles published before 7 June 2025 in three electronic databases: PubMed, Embase, and Cochrane Library. We then extracted authors, year of publication, country in which the study was conducted, study institution, study design, survival analysis, months of follow-up, size of study cohort, treatment, hazard ratios (HR) with 95% confidence interval (CI), and source of HR. The Newcastle-Ottawa Scale was used to analyze the risk and quality of included studies. All procedures were performed according to the PRISMA guidelines. A meta-analysis was performed on studies with sufficient survival data to analyze. Overall survival was analyzed to clarify the efficacy of metastasectomy for metastatic BCa. RESULTS:Our meta-analysis identified 8 of 7877 articles published from 1992 to 2024 that met our criteria. These articles encompassed 15 139 patients in all, which showed that patients who underwent metastasectomy had better overall survivals (OS) than did those who only had non-surgical treatment (HR = 0.46, 95% CI 0.27-0.79, I2 = 64.3%). Additionally, we found a positive association between OS and metastasectomy in patients with brain metastases (HR = 0.49, 95% CI 0.34-0.70, I2 = 49.7%). CONCLUSIONS:We found a positive association between metastasectomy and OS in patients with metastatic BCa, especially those with brain metastases. Metastasectomy should be considered an adequate approach in BCa, when feasible. TRIAL REGISTRATION:PROSPERO number: CRD42021234305.

Purpose:This study aimed to construct and validate nomograms for the prediction of overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS) in patients with resectable bladder urothelial carcinoma (BUC) after radical cystectomy (RC). Methods:We retrospectively collected the demographic, pathological, imaging, and laboratory data from patients with BUC who underwent RC. The training cohort included patients from the Affiliated Hospital of Qingdao University from January 2018 to December 2021, while the test cohort included patients from the same hospital between January 2016 and December 2017, along with patients from Qilu Hospital of Shandong University. Univariate and multivariate Cox regression analyses were conducted to identify independent predictors of OS, CSS, and DFS. The performance of the nomograms was evaluated using Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), the corrected AUC following 1,000 bootstrap resamplings with calibration curves, and decision curve analysis in both cohort validations. Results:A total of 393 patients were included in the training cohort, while 156 patients comprised the test cohort. Multivariate analyses revealed that age, tumor size, lymph node metastasis (LNM), lymphovascular invasion (LVI), urea nitrogen, creatinine, and the albumin/fibrinogen ratio (AFR) were independent predictors for OS. For CSS, the independent predictors were tumor size, LNM, LVI, urea nitrogen, and AFR. LNM and LVI were the independent predictors for DFS. The nomograms for OS and CSS demonstrated high predictive accuracy with robust CC-indexes and ROC curves, along with reliable calibration curves with corrected AUCs and clinical utility in both cohorts. The DFS nomogram also showed high predictive accuracy with stable corrected AUCs in both cohorts. Conclusion:We constructed OS, CSS, and DFS nomograms to predict prognosis in patients with BUC treated with RC. These nomograms exhibited high accuracy, reliability, and clinical utility in predicting outcomes in both cohorts.

BackgroundSarcomatoid renal cell carcinoma (sRCC) is an aggressive subtype with a poor prognosis. Preoperative prognostic tools are lacking, and the predictive value of sarcopenia combined with radiomic features from non-contrast CT remains unexplored.MethodsIn this retrospective study, 121 pathologically confirmed sRCC patients were enrolled. Sarcopenia was assessed using muscle mass measurements at the L3 level on preoperative non-contrast CT. Radiomic features were extracted from tumor regions of interest. Least absolute shrinkage and selection operator (LASSO) and Cox regression were used to select features and construct prognostic models for overall survival (OS). A combined model integrating sarcopenia status and radiomic signature (Rad-score) was developed and evaluated regarding its discrimination, calibration, and clinical utility.ResultsMultivariable analysis identified paravertebral muscle-defined sarcopenia (HR = 3.046, p = 0.029), platelet-to-neutrophil ratio, hemoglobin-albumin-lymphocyte-platelet score, tumor size, and N stage as independent prognostic factors. The combined model (clinical + Rad-score) demonstrated superior predictive performance for 1-, 2-, and 3-year OS, with AUCs of 0.849, 0.804, and 0.819, respectively, and significantly outperformed the radiomics-only model (p = 0.002). Calibration curves and decision curve analysis confirmed its clinical applicability.ConclusionThe integration of sarcopenia and non-contrast CT radiomics provides a valuable preoperative tool for predicting survival in sRCC patients, facilitating individualized risk stratification and clinical decision-making.

OBJECTIVE:This study employed bibliometric analysis to explore global research on metabolic syndrome (MetS) and bladder cancer (BC), focusing on characteristics and research trends. Additionally, a meta-analysis was conducted to comprehensively evaluate the association between MetS and its components with the risk of BC. METHODS:We conducted a comprehensive search of publications from 2002 to 2022 in the Web of Science Core Collection (WoSCC). Visualization analysis was performed using the Open Scientometrics Data Analysis and Visualization Platform, VOSviewer software and the R package "bibliometrix". For the meta-analysis, data from PubMed, Embase and the Cochrane Library up to March 22, 2022, were utilized. Literature from PubMed, Embase, Cochrane and Web of Science up to March 25, 2022, were retrieved, and data extraction was independently performed by two authors. A random-effects model was used to calculate pooled odds ratios (ORs) and 95% confidence intervals (95% CIs). Meta-analysis was conducted using RevMan 5.4 software. RESULT:In the bibliometric analysis, 147 papers were included, and information on countries, institutions, authors, journals and keywords from Web of Science was analyzed and visualized. For the meta-analysis, 11 studies involving 665,164 patients were included. The pooled analysis of six case-control studies showed that patients with MetS had a higher risk of BC compared to the non-MetS control group (OR = 1.62, 95% CI: 1.08-2.43, P < 0.01). Analysis of MetS components revealed that diabetes (OR = 0.44, 95% CI: 0.32-0.61, P < 0.01), low high-density lipoprotein (HDL) (OR = 0.29, 95% CI: 0.19-0.44, P < 0.01) and high triglycerides (OR = 0.59, 95% CI: 0.39-0.88, P < 0.01) were associated with an increased risk of BC. In contrast, hypertension (OR = 0.84, 95% CI: 0.62-1.12, P > 0.05) and obesity (OR = 0.8, 95% CI: 0.44-1.45, P > 0.05) showed no significant association with BC risk. CONCLUSION:This study provided valuable insights into the association between MetS and BC risk by identifying past research trends and hotspots. MetS and its components, such as diabetes, low HDL and high triglycerides, were associated with an increased risk of BC.

ABSTRACT:Fibrinogen-like protein 1 (FGL1), a liver-secreted protein involved in proliferation and metabolism, and lymphocyte activation gene 3 (LAG3), an immune checkpoint receptor expressed on the surfaces of various activated immune cells, play critical roles in tumor immunology. Numerous studies have confirmed that FGL1 acts as a ligand for LAG3 and mediates immune evasion by tumor cells. This review aims to provide a comprehensive summary of the research progress in FGL1/LAG3 in terms of its expression, role in the tumor microenvironment, and clinical application. The expression and regulation of FGL1/LAG3 are influenced by multiple cytokines and signaling pathways. In the tumor microenvironment, FGL1/LAG3 modulates tumor cell proliferation, invasion, and migration through mechanisms such as epithelial-mesenchymal transition, gene methylation, oxygen metabolism, and lipid metabolism. FGL1/LAG3 can serve as a prognostic biomarker, independently or in combination with PD-L1/PD-1, and can be targeted using monoclonal antibodies, bi-specific antibodies, and dual-targeted vaccines to restore the proliferation and activation potential of T cells. Additionally, FGL1/LAG3 has demonstrated therapeutic potential when combined with targeted therapies, radiotherapy, traditional Chinese medicine, and adoptive cell therapy. Overall, FGL1/LAG3 plays a pivotal role in cancer initiation, progression, diagnosis, treatment, and prognosis.

Prostate cancer (PCa) pathogenesis involves complex interactions between genetic susceptibility and exposure to endocrine-disrupting chemicals (EDCs). This study aimed to systematically identify key genes linking EDC exposure to PCa using an integrated bioinformatics and machine learning (ML) approach. We analyzed four transcriptomic datasets (GSE32571, GSE71016, GSE46602, GSE200879) and identified 437 differentially expressed genes (DEGs). By integrating these with high-confidence EDC-interacting genes from the Comparative Toxicogenomics Database (CTD), we pinpointed 169 core candidates. An ensemble ML framework, evaluating over 70 algorithm combinations, identified an optimal model (glmBoost + RF) that refined this list to eight core genes, including Glutathione S-Transferase Pi 1 (GSTP1). Mendelian randomization (MR) analysis established a causal, protective role for GSTP1 against PCa risk (OR = 0.880, 95 % CI = 0.777-0.998, P = 0.046). Molecular docking and dynamics simulations revealed stable binding between GSTP1 and high-priority EDCs, such as Benzo[a]pyrene (binding energy: -9.8 kcal/mol), indicating a direct interaction mechanism. Functional enrichment analyses implicated these genes in oxidative stress response and xenobiotic metabolism. Furthermore, single-cell RNA sequencing and immune infiltration analysis suggested a role for GSTP1 in modulating the tumor microenvironment. Our findings elucidate a critical "EDC-GSTP1-PCa" axis, highlighting GSTP1's potential as a therapeutic target and providing mechanistic insights into environmental chemical-induced prostate carcinogenesis.

Background In this study of patients with prostate cancer, we explored associations between low prostate-specific antigen (PSA) concentrations and disease progression as well as prognosis. Methods We retrospectively reviewed data of 233,554 prostate cancer patients in the Surveillance, Epidemiology and End Results (SEER) program and of 199 prostate cancer patients from the medical records of the Affiliated Hospital of Qingdao University with PSA ≤10 ng/mL at diagnosis. The patients were stratified into eight subgroups by T stage and Gleason score (GS) and survival curves for the resultant subgroups plotted using the Kaplan–Meier method. Multivariate Cox analyses were performed to investigate the effects of PSA concentrations in different subgroups. After randomly dividing patients into a training set and an internal validation set with a ratio of 7:3, a nomogram model to predict the survival of prostate cancer patients was subsequently established and validated. Results In all prostate cancer patients with Gleason score (GS) 8–10, low PSA concentrations were significantly associated with advanced disease and poor prognosis, functioning as a statistically significant risk factor. Conversely, in patients with GS 6–7 and Stage T1 disease, low PSA concentrations acted as a protective factor. A nomogram model for predicting prognosis was established and validated. We obtained similar results with an external validation cohort. Conclusions Our findings indicate that low PSA concentrations exert divergent impacts on prostate cancer patients stratified by T stage and GS. Specifically, in patients with high GS (8–10), low PSA concentrations represent a risk factor for disease progression to advanced stages and poor prognosis. Additionally, we developed a novel nomogram that effectively predicts survival outcomes in these patients.

Urinary tract infections (UTIs) are a common health issue affecting individuals worldwide. Recurrent urinary tract infections (rUTI) pose a significant clinical challenge, with limited understanding of the underlying mechanisms. Recent research suggests that the urobiome, the microbial community residing in the urinary tract, may play a crucial role in the development and recurrence of urinary tract infections. However, the specific virulence factor genes (VFGs) driven by urobiome contributing to infection recurrence remain poorly understood. Our study aimed to investigate the relationship between urobiome driven VFGs and recurrent urinary tract infections. By analyzing the VFGs composition of the urinary microbiome in patients with rUTI compared to a control group, we found higher alpha diversity in rUTI patients compared with healthy control. And then, we sought to identify specific VFGs features associated with infection recurrence. Specifically, we observed an increased abundance of certain VGFs in the recurrent infection group. We also associated VFGs and clinical data. We then developed a diagnostic model based on the levels of these VFGs using random forest and support vector machine analysis to distinguish healthy control and rUIT, rUTI relapse and rUTI remission. The diagnostic accuracy of the model was assessed using receiver operating characteristic curve analysis, and the area under the ROC curve were 0.83 and 0.75. These findings provide valuable insights into the complex interplay between the VFGs of urobiome and recurrent urinary tract infections, highlighting potential targets for therapeutic interventions to prevent infection recurrence.

Objectives Sarcomatoid renal pelvis carcinoma (SRPC) is a rare variant of RPC. We aimed to summarize the clinicopathological features and prognostic factors of SRPC. Methods In this retrospective study, we collected data from 24 patients with SRPC who were treated at the Department of Urology, Affiliated Hospital of Qingdao University between 2008 and 2021. The clinicopathological features of the patients were obtained from their medical records to evaluate the diagnosis, prognostic factors, and response to systemic therapy. Results Immunohistochemical staining revealed that cytokeratin was expressed in 19 patients with SRPC, while vimentin was expressed in all patients. Computer tomography showed these tumors as low-density (n = 12) or mixed-density masses, with or without necrotic areas (n = 12). All patients showed different degrees of enhancement on computed tomography. Lymph node metastasis was present in 6 patients and distant metastasis in 5. The median survival of all patients was 28 months. Patients without metastasis had a median survival of 46 months compared with 18 months in those with metastasis (P < 0.05). Necrosis had no significant influence on prognosis (P > 0.05). The median survival of patients with and without hydronephrosis was 18 and 104 months (P < 0.05). Among patients without metastasis, those without hydronephrosis survived longer than those with hydronephrosis (104 vs 18 months, P < 0.05), and necrosis had no effect on prognosis. In patients with metastasis, necrosis and hydronephrosis had no effect on prognosis (P > 0.05). Conclusion The prognosis of SRPC is poor, and the clinical stage, particularly the presence of distant metastasis, has a significant impact on prognosis.

Background Lymph node metastasis (LNM) is associated with worse prognosis in bladder urothelial carcinoma (BUC) patients. This study aimed to develop and validate machine learning (ML) models to preoperatively predict LNM in BUC patients treated with radical cystectomy (RC).Methods We retrospectively collected demographic, pathological, imaging, and laboratory information of BUC patients who underwent RC and bilateral lymphadenectomy in our institution. Patients were randomly categorized into training set and testing set. Five ML algorithms were utilized to establish prediction models. The performance of each model was assessed by the area under the receiver operating characteristic curve (AUC) and accuracy. Finally, we calculated the corresponding variable coefficients based on the optimal model to reveal the contribution of each variable to LNM.Results A total of 524 and 131 BUC patients were finally enrolled into training set and testing set, respectively. We identified that the support vector machine (SVM) model had the best prediction ability with an AUC of 0.934 (95% confidence interval [CI]: 0.903-0.964) and accuracy of 0.916 in the training set, and an AUC of 0.855 (95%CI: 0.777-0.933) and accuracy of 0.809 in the testing set. The SVM model contained 14 predictors, and positive lymph node in imaging contributed the most to the prediction of LNM in BUC patients.Conclusions We developed and validated the ML models to preoperatively predict LNM in BUC patients treated with RC, and identified that the SVM model with 14 variables had the best performance and high levels of clinical applicability.

INTRODUCTION:This study explored the predictors of upstaging and multiple sites of extension, and constructed a predictive model based on perioperative characteristics to calculate the risk of upstaging of cT1 renal cell carcinoma to pT3. METHODS:We retrospectively reviewed 1012 patients diagnosed with cT1 renal cell carcinoma who underwent surgical treatment at the Affiliated Hospital of Qingdao University between June 2016 and August 2021. The continuous and categorical variables were analyzed using the Mann-Whitney U test and Chi-square test, respectively. After randomly dividing patients into a training set and an internal validation set with a ratio of 7:3, univariate and multivariate logistic regression analyses were used to explore the predictors of upstaging and multiple sites of extension. A nomogram model was established based on the predictors of upstaging and was validated. RESULTS:Ninety-one cases (8.99%) of renal cell carcinoma were upstaged to pT3. In the training set, multivariate logistic regression identified the following predictors of upstaging: maximum tumor diameter, hilus involvement, tumor necrosis, tumor edge irregularity, symptoms, smoking, and platelet-lymphocyte ratio. A nomogram model was established based on the predictors. The area under the receiver operating characteristic curve was 0.810 in the training set, and 0.804 in the validation set. A 10-fold internal cross-validation conducted 200 times showed that the mean area under the curve was 0.797. The calibration curve and decision curve analysis suggested that the nomogram had robust clinical predictive power. Analyses showed higher neutrophil-lymphocyte ratio and tumor necrosis were associated with multiple sites of extrarenal extension in patients with pT3a renal cell carcinoma. CONCLUSIONS:We identified 7 predictors of upstaging to pT3 and 2 predictors of multiple sites of extension. A nomogram model was constructed with satisfactory accuracy for predicting upstaging to pT3.

This study aimed to validate the prognostic value of a four-tiered grading system recently proposed by Avulova et al. and to explore the prognostic ability of another four-tiered classification grading system in which there is a separate Grade 3 for tumor necrosis. Grading of chromophobe renal cell carcinoma (ChRCC) by the Fuhrman system is not feasible because of the inherent nuclear atypia in ChRCC. We collected relevant data of 263 patients with ChRCC who had undergone surgery in our hospital from 2008 to 2020. The Kaplan–Meier method was used to calculate the survival rate and Cox proportional hazard regression models to assess associations with cancer-specific survival and distant metastasis-free survival by hazard ratios (HRs) and 95